Abstract: A self-driving laboratory system and associated methods for autonomous scientific experimentation are disclosed. The system integrates automated laboratory instrumentation, multi-modal sensors, and artificial intelligence models to design, execute, and iteratively optimize biological or chemical experiments. A computing platform receives research objectives, generates experimental protocols, and controls laboratory instruments to perform experimental procedures. Experimental data are collected from imaging systems, analytical instruments, and environmental sensors and are integrated using machine learning and sensor fusion techniques to estimate experimental state. Optimization algorithms, including probabilistic and multi-objective models, analyze the data to generate modified experimental protocols that improve experimental outcomes. The system may dynamically adjust environmental parameters and measurement strategies during experimentation and may share learning across distributed laboratory sites.

Abstract: A method of drilling is disclosed and includes pumping a wellbore fluid into a wellbore through an earth formation, wherein the wellbore fluid comprises a base fluid and a surface active agent capable of altering wettability of fines located in the earth formation, and allowing filtration of at least a portion of the wellbore fluid into the earth formation.

Abstract: A method of drilling is disclosed and includes pumping a wellbore fluid into a wellbore through an earth formation, wherein the wellbore fluid comprises a base fluid and a surface active agent capable of altering wettability of fines located in the earth formation, and allowing filtration of at least a portion of the wellbore fluid into the earth formation.